#mini-batch的实现
import sys, os
sys.path.append(os.pardir)
import numpy as np
from dataset.mnist import load_mnist
from two_layer_net import TwoLayerNet

(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label = True)

#超参数
iters_num = 20
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1

train_loss_list = []
train_acc_list = []
test_acc_list = []
#平均每个epoch的重复次数
iter_per_epoch = max(train_size / batch_size, 1)

network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)

for i in range(iters_num):
    #获取mini-batch
    print('第 ' + str(i+1) + '轮训练……')
    batch_mask = np.random.choice(train_size, batch_size)
    x_batch = x_train[batch_mask]
    t_batch = t_train[batch_mask]

    #计算梯度
    grad = network.numerical_gradient(x_batch, t_batch)
    #grad = network.gradient(x_batch, t_batch)  #高速版

    #更新参数
    for key in ('W1', 'b1', 'W2', 'b2'):
        network.params[key] -= learning_rate * grad[key]

    #记录学习过程
    loss = network.loss(x_batch, t_batch)
    print('loss: ' + str(loss))
    train_loss_list.append(loss)
    #if i % iter_per_epoch == 0:
    train_acc = network.accuracy(x_train, t_train)
    test_acc = network.accuracy(x_test, t_test)
    train_acc_list.append(train_acc)
    test_acc_list.append(test_acc)
    print('train acc, test acc, chazhi | ' + str(train_acc) + ' , ' + str(test_acc) + ' , ' + str(train_acc-test_acc))
